Ozcelik Salih T A, Uyanık Hakan, Deniz Erkan, Sengur Abdulkadir
Electrical-Electronics Engineering Department, Engineering Faculty, Bingol University, Bingol 12000, Turkey.
Electrical-Electronics Engineering Department, Engineering Faculty, Munzur University, Tunceli 62000, Turkey.
Diagnostics (Basel). 2023 Jan 4;13(2):182. doi: 10.3390/diagnostics13020182.
Blood pressure is the pressure exerted by the blood in the veins against the walls of the veins. If this value is above normal levels, it is known as high blood pressure (HBP) or hypertension (HPT). This health problem which often referred to as the "silent killer" reduces the quality of life and causes severe damage to many body parts in various ways. Besides, its mortality rate is very high. Hence, rapid and effective diagnosis of this health problem is crucial. In this study, an automatic diagnosis of HPT has been proposed using ballistocardiography (BCG) signals. The BCG signals were transformed to the time-frequency domain using the spectrogram method. While creating the spectrogram images, parameters such as window type, window length, overlapping rate, and fast Fourier transform size were adjusted. Then, these images were classified using ConvMixer architecture, similar to vision transformers (ViT) and multi-layer perceptron (MLP)-mixer structures, which have attracted a lot of attention. Its performance was compared with classical architectures such as ResNet18 and ResNet50. The results obtained showed that the ConvMixer structure gave very successful results and a very short operation time. Our proposed model has obtained an accuracy of 98.14%, 98.79%, and 97.69% for the ResNet18, ResNet50, and ConvMixer architectures, respectively. In addition, it has been observed that the processing time of the ConvMixer architecture is relatively short compared to these two architectures.
血压是血液在静脉中对静脉壁施加的压力。如果这个值高于正常水平，就被称为高血压（HBP）或高血压症（HPT）。这个经常被称为“无声杀手”的健康问题会降低生活质量，并以各种方式对许多身体部位造成严重损害。此外，其死亡率非常高。因此，对这个健康问题进行快速有效的诊断至关重要。在本研究中，提出了一种使用心冲击图（BCG）信号对高血压症进行自动诊断的方法。使用频谱图方法将BCG信号转换到时间-频率域。在创建频谱图图像时，调整了窗口类型、窗口长度、重叠率和快速傅里叶变换大小等参数。然后，使用类似于视觉Transformer（ViT）和多层感知器（MLP）-混合器结构的ConvMixer架构对这些图像进行分类，这些结构已经引起了广泛关注。将其性能与ResNet18和ResNet50等经典架构进行了比较。得到的结果表明，ConvMixer结构给出了非常成功的结果，并且运行时间非常短。我们提出的模型在ResNet18、ResNet50和ConvMixer架构上分别获得了98.14%、98.79%和97.69%的准确率。此外，还观察到ConvMixer架构的处理时间与这两种架构相比相对较短。
